H− state estimation for discrete-time complex networks with linear fractional uncertainties

This paper is concerned with the state estimation problem for a class of discrete time-delay nonlinear complex networks with linear fractional uncertainties. The nonlinear functions are described by the sector-like nonlinearities that are more general than the commonly used Lipschitz ones. The purpose of the addressed problem is to design a state estimator to estimate the network states through available output measurements such that the dynamics of the estimation error is guaranteed to be globally asymptotically stable in the mean square and the effect from the exogenous disturbances to be estimation accuracy is attenuated a given level by means of an H_∞-norm. In terms of a novel Lyapunov-Krasovskii functional and the Kronecker product, sufficient conditions are established under which the addressed state estimation problem is recast as solving a convex optimization problem via the semi-definite programming method. Finally, a numerical example is applied to demonstrate the effectiveness of the proposed state estimation approach.